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      Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review

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          Abstract

          In the last few years, estimating ground reaction forces by means of wearable sensors has come to be a challenging research topic paving the way to kinetic analysis and sport performance testing outside of labs. One possible approach involves estimating the ground reaction forces from kinematic data obtained by inertial measurement units (IMUs) worn by the subject. As estimating kinetic quantities from kinematic data is not an easy task, several models and protocols have been developed over the years. Non-wearable sensors, such as optoelectronic systems along with force platforms, remain the most accurate systems to record motion. In this review, we identified, selected and categorized the methodologies for estimating the ground reaction forces from IMUs as proposed across the years. Scopus, Google Scholar, IEEE Xplore, and PubMed databases were interrogated on the topic of Ground Reaction Forces estimation based on kinematic data obtained by IMUs. The identified papers were classified according to the methodology proposed: (i) methods based on direct modelling; (ii) methods based on machine learning. The methods based on direct modelling were further classified according to the task studied (walking, running, jumping, etc.). Finally, we comparatively examined the methods in order to identify the most reliable approaches for the implementation of a ground reaction force estimator based on IMU data.

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          Adjustments to Zatsiorsky-Seluyanov's segment inertia parameters.

          P. de Leva (1996)
          Zatsiorsky et al. (in Contemporary Problems in Biomechanics, pp. 272-291, CRC Press, Massachusetts, 1990a) obtained, by means of a gamma-ray scanning technique, the relative body segment masses, center of mass (CM) positions, and radii of gyration for samples of college-aged Caucasian males and females. Although these data are the only available and comprehensive set of inertial parameters regarding young adult Caucasians, they have been rarely utilized for biomechanical analyses of subjects belonging to the same or a similar population. The main reason is probably that Zatsiorsky et al. used bony landmarks as reference points for locating segment CMs and defining segment lengths. Some of these landmarks were markedly distant from the joint centers currently used by most researchers as reference points. The purpose of this study was to adjust the mean relative CM positions and radii of gyration reported by Zatsiorsky et al., in order to reference them to the joint centers or other commonly used landmarks, rather than the original landmarks. The adjustments were based on a number of carefully selected sources of anthropometric data.
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            The technology of accelerometry-based activity monitors: current and future.

            This paper reviews accelerometry-based activity monitors, including single-site first-generation devices, emerging technologies, and analytical approaches to predict energy expenditure, with suggestions for further research and development. The physics and measurement principles of the accelerometer are described, including the sensor properties, data collections, filtering, and integration analyses. The paper also compares these properties in several commonly used single-site accelerometers. The emerging accelerometry technologies introduced include the multisensor arrays and the combination of accelerometers with physiological sensors. The outputs of accelerometers are compared with criterion measures of energy expenditure (indirect calorimeters and double-labeled water) to develop mathematical models (linear, nonlinear, and variability approaches). The technologies of the sensor and data processing directly influence the results of the outcome measurement (activity counts and energy expenditure predictions). Multisite assessment and combining accelerometers with physiological measures may offer additional advantages. Nonlinear approaches to predict energy expenditure using accelerometer outputs from multiple sites and orientation can enhance accuracy. The development of portable accelerometers has made objective assessments of physical activity possible. Future technological improvements will include examining raw acceleration signals and developing advanced models for accurate energy expenditure predictions.
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              Muscle contributions to propulsion and support during running.

              Muscles actuate running by developing forces that propel the body forward while supporting the body's weight. To understand how muscles contribute to propulsion (i.e., forward acceleration of the mass center) and support (i.e., upward acceleration of the mass center) during running we developed a three-dimensional muscle-actuated simulation of the running gait cycle. The simulation is driven by 92 musculotendon actuators of the lower extremities and torso and includes the dynamics of arm motion. We analyzed the simulation to determine how each muscle contributed to the acceleration of the body mass center. During the early part of the stance phase, the quadriceps muscle group was the largest contributor to braking (i.e., backward acceleration of the mass center) and support. During the second half of the stance phase, the soleus and gastrocnemius muscles were the greatest contributors to propulsion and support. The arms did not contribute substantially to either propulsion or support, generating less than 1% of the peak mass center acceleration. However, the arms effectively counterbalanced the vertical angular momentum of the lower extremities. Our analysis reveals that the quadriceps and plantarflexors are the major contributors to acceleration of the body mass center during running. Copyright © 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                05 August 2018
                August 2018
                : 18
                : 8
                : 2564
                Affiliations
                Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland; salvatore.tedesco@ 123456tyndall.ie (S.T.); john.barton@ 123456tyndall.ie (J.B.); brendan.oflynn@ 123456tyndall.ie (B.O.)
                Author notes
                [* ]Correspondence: andrea.ancillao@ 123456hotmail.com ; Tel.: +353-212346189
                Author information
                https://orcid.org/0000-0002-3401-967X
                Article
                sensors-18-02564
                10.3390/s18082564
                6111315
                30081607
                835c45e3-e3e5-4e86-98be-08b04328d6ef
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 June 2018
                : 28 July 2018
                Categories
                Review

                Biomedical engineering
                biomechanical modelling,ground reaction forces,inertial measurements,inertial measurement units (imu),kinetics,machine learning,wearable sensors

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